Biotreatment of a triphenylmethane dye solution using a Xanthophyta alga: Modeling of key factors by neural network

Khataee, Alireza; Zarei, Mahmoud; Dehghan, Gholamreza; Ebadi, E.; Pourhassan, M.

doi:10.1016/j.jtice.2010.08.006

Cited by 51 publications

(15 citation statements)

References 36 publications

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“…[21], removal of chlorophenol by coconut fiber carbon [22], biotreatment of a triphenylmethane dye solution (Malachite Green) by Vaucheria sp. [23], competitive adsorption of phenol and resorcinol by using carbonaceous adsorbents (activated carbon, wood charcoal and rice husk ash) [24], biotreatment of a triphenylmethane dye solution (Malachite Green) by Cladophora sp. [25], and removal of Lanaset Red G by Chara contraria [26].…”

Section: Photo 1 Posidonia Oceanica (L) Delile Meadows From Turkish mentioning

confidence: 99%

Artificial Neural Network Model for Biosorption of Methylene Blue by Dead Leaves of Posidonia Oceanica (L.) Delile

Demir¹,

Dural²,

Alyürük³

et al. 2012

NNW

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Genetic algorithms (GAs) are stochastic methods that are widely used in search and optimization. The breeding process is the main driving mechanism for GAs that leads the way to find the global optimum. And the initial phase of the breeding process starts with parent selection. The selection utilized in a GA is effective on the convergence speed of the algorithm. A GA can use different selection mechanisms for choosing parents from the population and in many applications the process generally depends on the fitness values of the individuals. Artificial neural networks (ANNs) are used to decide the appropriate parents by the new hybrid algorithm proposed in this study. And the use of neural networks aims to produce better offspring during the GA search. The neural network utilized in this algorithm tries to learn the structural patterns and correlations that enable two parents to produce high-fit offspring. In the breeding process, the first parent is selected based on the fitness value as usual. Then it is the neural network that decides the appropriate mate for the first parent chosen. Hence, the selection mechanism is not solely dependent on the fitness values in this study. The algorithm is tested with seven benchmark functions. It is observed from results of these tests that the new selection method leads genetic algorithm to converge faster.

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Section: Photo 1 Posidonia Oceanica (L) Delile Meadows From Turkish mentioning

confidence: 99%

Artificial Neural Network Model for Biosorption of Methylene Blue by Dead Leaves of Posidonia Oceanica (L.) Delile

Demir¹,

Dural²,

Alyürük³

et al. 2012

NNW

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“…Many treatment technologies have been applied in order to decolorize MG from an aqueous medium such as coagulation-flocculation [5,6], biodegradation [7], photocatalytic degradation [8], sonochemical and sonophotocatalytic degradation [9][10][11], ozonation [12], and Fenton reagent [13][14][15][16]. Among all these techniques, adsorption is regarded as simple, economically manageable, and desirable process for the treatment of industrial effluent due to its high efficiency and ability to separate a wide range of pollutants.…”

Section: Introductionmentioning

confidence: 99%

Nanoparticles Loaded Biopolymer as Effective Adsorbent for Adsorptive Removal of Malachite Green from Aqueous Solution

Raval

Shah

2016

Water Conserv Sci Eng

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In the present study, the biocompatible nanocomposite (nickel oxide nanoparticles loaded chitin beads (NiO NPs/CH)) was synthesized, characterized by using scanning electron microscopy (SEM) and Fourier transform infrared (FT-IR) analysis and then study its prospective application towards adsorption of malachite green (MG) dye. Further, batch studies were conducted to evaluate the adsorption capacity of nanocomposite, and the effects of various parameters, i.e., pH, adsorbate concentration, contact time, dosage of adsorbent and temperature, were investigated. The results revealed that the amount of MG adsorbed on the adsorbent increases with increasing initial dye concentration and by decreasing temperature. The equilibrium MG adsorption data on NiO NPs/CH were best described by the Langmuir isotherm model. Besides, the adsorption kinetics followed pseudosecond-order rate equation. Thermodynamic parameters such as free energy of adsorption (ΔG°), enthalpy (ΔH°), and entropy (ΔS°) changes were calculated, and the results suggested that the adsorption process was spontaneous and exothermic in nature. Based on the results, it was concluded that the nanocomposite can be sustainably prepared and efficiently used for the adsorptive removal of MG from colored aqueous solutions.

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“…Contact time with a relative importance of 48% appeared to be the most influential parameter in the sorption process of BR 46 on WH, followed by initial dye concentration (40%), particle size (5%), pH regime (4%), and temperature (3%). The most important input variables on the sorption process varied in the literature, such as contact time for biotreatment of Malachite Green by macroalga [22,23], initial dye concentration (41.43%) for sorption of Acid Black 172 on nonviable Penicillium YW 01 [24], contact time (31.16%) for biotreatment of Malachite Green by Vaucheria species [41], pH (23%) for the removal of methylene blue on the lignite [39], and pH (43%) for the removal of Lanaset Red G on WH [17].…”

Section: Importance Of Operating Factors From Annmentioning

confidence: 99%

“…In sorption study, it was also reported as R 2 = 0.98 for the prediction of an azo-metal complex dye onto lentil straw [37]. Additionally, genetic algorithms were used together with ANN in some sorption works, in which R 2 = 0.97 for the description of metal ions sorption on chitosan foamed structure-equilibrium [44], R 2 = 0.99 for the removal of reactive orange 12 by copper sulfide nanoparticlesactivated carbon [41].…”

Section: Development Of Gep Equation For Further Prediction Areasmentioning

confidence: 99%

Artificial neural network and genetic algorithms for modeling of removal of an azo dye on walnut husk

Çelekli

Bozkurt

Geyik

2015

Desalination and Water Treatment

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A B S T R A C TThe study dealt with an evaluating kinetic aspect of removal of Basic Red (BR) 46 by walnut husk (WH). Artificial neural network (ANN), gene expression programming (GEP), logistic, and pseudo-second-order kinetic models were constructed to predict the removal efficiency of BR 46 on WH. Spectra of WH before and after the sorption process were obtained using FTIR-ATR. Functional groups such as hydroxyl, carbonyl, and carboxyl groups had a significant role on the interaction between WH and BR 46. Maximum sorption was determined as 66.45 mg g −1 . About 2,160 experimental mean sets were used to feed ANN structure. ANN was found to be the best model due to its lowest error and highest determination of coefficient values. ANN showed that contact time was the most efficient parameter, followed by initial dye concentration for the sorption process. GEP model successfully described the sorption kinetic process as functions of pH, adsorbent particle size, initial dye concentration, contact time, and temperature in a single equation. Results of thermodynamic parameters indicated that this process is being feasible, endothermic, and spontaneous. Results revealed that WH had a great potential to remove BR 46 from aqueous solution at different environmental conditions.

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Biotreatment of a triphenylmethane dye solution using a Xanthophyta alga: Modeling of key factors by neural network

Cited by 51 publications

References 36 publications

Artificial Neural Network Model for Biosorption of Methylene Blue by Dead Leaves of Posidonia Oceanica (L.) Delile

Artificial Neural Network Model for Biosorption of Methylene Blue by Dead Leaves of Posidonia Oceanica (L.) Delile

Nanoparticles Loaded Biopolymer as Effective Adsorbent for Adsorptive Removal of Malachite Green from Aqueous Solution

Artificial neural network and genetic algorithms for modeling of removal of an azo dye on walnut husk

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